The Hidden Value of Narrative Comments for Assessment: A Quantitative Reliability Analysis of Qualitative Data
Bibliographic record
Abstract
PURPOSE: In-training evaluation reports (ITERs) are ubiquitous in internal medicine (IM) residency. Written comments can provide a rich data source, yet are often overlooked. This study determined the reliability of using variable amounts of commentary to discriminate between residents. METHOD: ITER comments from two cohorts of PGY-1s in IM at the University of Toronto (graduating 2010 and 2011; n = 46-48) were put into sets containing 15 to 16 residents. Parallel sets were created: one with comments from the full year and one with comments from only the first three assessments. Each set was rank-ordered by four internists external to the program between April 2014 and May 2015 (n = 24). Generalizability analyses and a decision study were performed. RESULTS: For the full year of comments, reliability coefficients averaged across four rankers were G = 0.85 and G = 0.91 for the two cohorts. For a single ranker, G = 0.60 and G = 0.73. Using only the first three assessments, reliabilities remained high at G = 0.66 and G = 0.60 for a single ranker. In a decision study, if two internists ranked the first three assessments, reliability would be G = 0.80 and G = 0.75 for the two cohorts. CONCLUSIONS: Using written comments to discriminate between residents can be extremely reliable even after only several reports are collected. This suggests a way to identify residents early on who may require attention. These findings contribute evidence to support the validity argument for using qualitative data for assessment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".